We select 20 high-quality images from the DIV2K [1]}, [2]} dataset as the original images in the experiments. These images are shown in Fig. 7. The proposed JND model is compared with six existing JND models including Yang2005 [3]}, Zhang2005 [4]}, Wu2013 [5]}, Wu2017 [6]}, Jakhetiya2018 [7]}, and Chen2020 [8]}. Note that all these compared JND models are designed based on the bottom-up design philosophy while the proposed one is the only top-down JND model as far as we have known. The detailed performance results of different JND models on noise injection are shown in Table I. As shown in Table I, our JND model achieves the best performance in MS-SSIM for all the images, and the best performance in MOS for all the images except the image `I12'. In addition, our proposed JND model achieves the best performance in both MS-SSIM and MOS at an average level when considering all the images. These experimental results demonstrate the superiority of our proposed JND method against others in guiding noise injection.
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